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Quantitative Information Fusion for Hydrological Sciences pp 1–18Cite as

Data Fusion Methods for Integrating Data-driven Hydrological Models

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Part of the book series: Studies in Computational Intelligence ((SCI,volume 79))

This chapter will address the use of different data fusion techniques for integrating or combining hydrological models. Different approaches will be demonstrated using flow forecasting models from the River Ouse catchment in the UK for a lead time of 6 hours. These approaches include simple averaging, neural networks, fuzzy logic, M5 model trees and instance-based learning. The results show that the data fusion approaches produce better performing models compared to the individual models on their own. The potential of this approach is demonstrated yet remains largely unexplored in real-time hydrological forecasting.

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Author information

Authors and Affiliations

  1. School of Geography, University of Leeds Leeds, LS2 9JT, UK

    Linda See

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  1. Linda See
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Editors and Affiliations

  1. Simula Research Laboratory, 134, 1325, Lysaker, Norway

    Xing Cai

  2. Department of Informatics, University of Oslo, 1080 Blindern, 0316, Oslo, Norway

    Xing Cai

  3. Department of Hydrology and Water Resources, The University of Arizona, Tucson, Arizona, 85721, USA

    T. -C. Jim Yeh

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© 2008 Springer-Verlag Berlin Heidelberg

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See, L. (2008). Data Fusion Methods for Integrating Data-driven Hydrological Models. In: Cai, X., Yeh, T.C.J. (eds) Quantitative Information Fusion for Hydrological Sciences. Studies in Computational Intelligence, vol 79. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75384-1_1

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  • DOI: https://doi.org/10.1007/978-3-540-75384-1_1

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-75383-4

  • Online ISBN: 978-3-540-75384-1

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